The Challenge:

Staff and faculty relied on emails and shared folders to find policies, academic rules, and procedures. Information existed but was difficult to locate, leading to repeated queries and delays.

The Solution: System responded only using approved documents by implemented a retrieval-based AI assistant connected to:

• Academic policies
• Examination rules
• Internal guidelines and SOPs

The Challenge:

Employees frequently asked similar questions about SOPs, safety protocols, and equipment usage. Admin teams were overwhelmed, and answers were inconsistent.

The Solution: Saved queries response time 2 days to 2 minutes and admin workload reduced by 40%

• Deployed RAG24 to access internal documents and manuals
• Connected it to workflow automation so employees could ask questions via chat/email
• Answers were automatically pulled from verified internal sources only

The Challenge:

Equipment failures (HVAC, pumps, and elevators) were reported only after breakdowns occurred. This caused downtime, guest complaints, and emergency maintenance costs.

The Solution: A machine learning model was trained using

• Sensor readings
• Maintenance logs
• Past failure data

The Challenge:

The university had an ML model to identify at-risk students, but faculty did not trust or use it. Predictions were inconsistent, hard to explain, and often out of sync with real classroom behavior

The Solution: Improvise the ML system by fixing data and gain trust not rebuild the whole model

• Cleaned and standardized the input data
• Removed unstable and irrelevant features
• Stabilized model retraining cycles